Prisoner tracking and warning system and corresponding methods

ABSTRACT

A system and method for tracking, monitoring and learning prisoner or parolee behavior involves obtaining prisoner or parolee data and monitoring data for at least one individual prisoner or parolee, storing the prisoner or parolee data and monitored data into a database, learning prisoner or parolee behavior from the prisoner or parolee data and the monitored data in the database, and updating the prisoner or parolee data and the monitored data in the database. Expert system (i.e. including but not limited to fuzzy logic, reinforcement learning, neural networks, artificial intelligence, etc.) algorithms are executed for determining and analyzing deviated behavior by the prisoner or parolee. A parole level is assigned to the prisoner or parolee and it is determined whether the prisoner or parolee is to be moved up or down a parole level depending on whether the prisoner or parolee behavior does not constitute or does constitute prisoner or parolee violations. Furthermore, the system tracks, monitors, and learns the behavior of the prisoner or parolee by controlling and regulating the permitted/prohibited locations or sectors, the permitted/prohibited location or sector dwell times, the permitted/prohibited travel routes, the permitted/prohibited travel times that the prisoner or parolee spends at or between various locations.

BACKGROUND OF INVENTION

The inventions described herein relate to the field of prisoner orparolee tracking and warning systems and methods, and more specifically,to comprehensive prisoner or parolee tracking systems and methods usingthe Global Positioning System ("GPS") to track the movements ofprisoners/parolees and an expert system to continually learn,distinguish and report normal and abnormal or prohibited behavior byprisoners/parolees. Additional prisoner/parolee sensors are used todetect and report substance abuse or other alarming or threateningsituations.

The control and/or confinement of prisoners/parolees is a complex andexpensive problem. The ever increasing rate of various crimes requiresincarceration of thousands of persons every year in the United States.Such detentions are extremely costly, requiring elaborate prison systemswith attendant physical facilities and large staffs to monitor prisoneractivities and to care for prisoners. Yet many crimes for which peopleare incarcerated do not necessarily represent a severe threat tosociety. Examples include selected, non-violent or minor offenses ormisdemeanors, such as petty theft, shoplifting, etc. In addition, manyprisoners, having served portions of prescribed sentences, couldpossibly be paroled if effective prisoner/parolee tracking, monitoring,and learning systems and methods were available to enable surveillanceand continual detecting and learning of their activities while onparole. By restricting the areas within which parolees may move and thetimes that they may spend traveling or may spend at specific locations,the possibility of repeat offenses may be minimized. As a result,valuable prison space may be reserved for more serious offenders.

There is an increasing interest in remote confinement monitoring systemsand methods for monitoring prisoners or parolees. Such systems typicallyinvolve a type of house arrest or house detention. Various methods havebeen described in issued patents for determining whether or not aprisoner or parolee is at a specified location, such as at his house.Field Monitoring Devices (FMD) are sometimes used to record informationconcerning prisoner or parolee presence. This information is typicallytransmitted to a centralized control center. Various forms of electronicmonitoring technology and identification tags have been previouslydescribed for identifying prisoners or parolees and monitoring theirgeneral status or behavior. Voice verification methods have beendescribed or taught for identifying particular prisoners or parolees toinsure their presence at specified location. Secured straps andtamper-indicating fastening mechanisms that generate alarms if removalis attempted have been disclosed for attaching tags or otheridentification mechanisms to prisoners or parolees.

However, none of these prior art house arrest or house incarcerationsystems and methods are known to enable tracking, monitoring andlearning of prisoners or parolees and their respective movement orbehavior over extended areas nor do they verify travel routes, lengthsof times given at locations, lengths of time traveling, avoidance ofprohibited areas, and deviations from normal or expected behavior. Also,prior art systems are not capable of actively learning and adapting to aprisoner's or parolee's permissible behavior patterns and reportingdeviations from those permissible patterns to a prisoner/parolee controlcenter. Furthermore, the prior art systems do not utilize expert systemsor algorithms (i.e. including but not limited to fuzzy logic, neuralnetworks, reinforcement learning, etc.) in providing the capabilities oflearning behavior, movement, or patterns of a prisoner or parolee, or ofreinforcing acceptable prisoner or parolee behavior by rewarding theprisoner or parolee for proper activities. In addition, prior artprisoner tracking, monitoring, and learning systems have not fullyintegrated in combination together the capabilities of modern GPStechnology, electronic monitoring for detecting substance abuse, andother sensors to detect unusual or suspicious events in the vicinity ofprisoners or parolees being tracked and monitored.

Various house arrest, house incarceration and remote confinement systemsand methods including systems with electronic monitoring, restrainingmechanism, and tamper free security monitoring devices attached toprisoners or parolees are described in the following documents, each ofwhich is incorporated herein by reference: U.S. Pat. Nos. 4,816,377;4,918,425; 4,918,432; 4,924,211; 4,943,885; 4,952,913; 4,952,928;4,980,671; 4,999,613; 5,023,901; 5,032,823; 5,075,670; 5,103,474;5,117,222; 5,146,207; 5,170,426; 5,182,543; 5,204,670; 5,206,897;5,218,344; 5,255,306; 5,266,944; 5,298,884; 5,341,126; 5,369,394;5,448,221; 5,455,851; 5,461,390; and 5,471,197.

In addition many patents have been issued for various applications ofGPS for locating and tracking objects and for navigation purpose.Various configurations of GPS-based tracking and communication systemsand methods are described in the following documents, each of which isincorporated herein by reference: The Navstar Global Positioning Systemby Tom Logsdon, Van Nostrand and Reinhold, New York (1992), ISBN0-422-01040-0; GPS Satellite Surveying by Alfred Leick, John Wiley &Sons, New York (1990), ISBN 0-471-81990-05; GPS--A Guide to the NextUtility by Jeff Hurn, Trimble Navigation, Ltd., Sunnyvale, Calif.(1989); Differential GPS Explained by Jeff Hurn, Trimble NavigationLtd., Sunnyvale, Calif. (1993); and U.S. Pat. Nos.: 5,182,566;5,187,805; 5,202,829; 5,223,844; 5,225,842; 5,323,322; 5,243,652;5,345,244; 5,359,332; 5,379,244; 5,382,958; 5,389,934; 5,390,125;5,396,540; 5,408,238; 5,414,432; 5,418,537; 5,422,813; 5,422,816;5,430,656; and 5,434,787.

Furthermore, expert systems (i.e. including but not limited to fuzzylogic, neural networks, reinforcement learning, etc.) are well known tothose of ordinary skill in the art, as reflected in the followingpublications, each of which is incorporated by reference herein: Harmon,Paul and King, David, Artificial Intelligence in Business--ExpertSystems, John Wiley & Sons, New York (1985), ISBN 0-471-81554-3;Gottinger, H. and Weimann, H., Artificial Intelligence--a tool forindustry and management, Ellis Horwood, New York (1990), ISBN0-13-048372-9; Mirzai, A. R., Artificial Intelligence--Concepts andapplications in engineering, Chapman and Hall, New York (1990), ISBN0-412-37900-7; Bourbakis, N., Artificial Intelligence Methods andApplications, World Scientific, New Jersey (1992), ISBN 981-02-1057-4;Schalkoff, R., Artificial Intelligence: An Engineering Approach,McGraw-Hill, New York (1990), ISBN 0-07-055084-0; Frenzel Jr., L., CrashCourse in Artificial Intelligence and Expert Systems, Howard W. Sams &Co., Indianapolis, Ind. (1987), ISBN 0-672-22443-7. However, expertsystems, fuzzy logic, neural networks, reinforcement learning, etc. donot appear to have been used or applied in the prisoner behaviortracking, monitoring, and learning areas.

Various techniques have also been disclosed and implemented formonitoring vital signs of persons, including breath analyzers, sweatanalyzers, and heart rate monitors. However, a totally integratedprisoner/parolee monitoring and tracking system and method that makesoptimum use of location, travel, and GPS tracking, physical monitoring,security system technology, and expert systems and methods is notdisclosed in the prior art.

SUMMARY OF INVENTION

It is an object of these inventions to provide new and usefulprisoner/parolee tracking, monitoring, warning and learning andreinforcing systems and methods that permit tracking the movements ofprisoners/parolees, while at the same time learning, updating andreporting unusual or prohibited travel of a prisoner/parolee.

It is a further object of these inventions to provide prisoner orparolee tracking, monitoring, warning, and learning systems and methodsthat make use of the existing Global Positioning System ("GPS") topermit accurate determination of the location of individualprisoners/parolees.

Another object of these inventions is to provide prisoner/paroleetracking, monitoring, warning, and learning systems and methods thatpermit definition of specific authorized locations or destinations foreach prisoner/parolee.

It is yet another object of these inventions to provide prisoner/paroleetracking, monitoring, warning, and learning systems and methods thatpermit comparison of travel times of a prisoner/parolee betweenlocations with predetermined or predicted travel times to ensure thatthe prisoner/parolee proceeds directly between prescribed points in hisor her travels.

It is another object of these inventions to provide predetermined timedurations specifying a maximum permitted time that a prisoner/paroleemay stay at a specific location.

It is yet another object of these inventions to permit definition ofareas to which the prisoner/parolee may travel to or through to theexclusion of other areas, and to reward extended periods of goodbehavior with expanded areas and durations of authorized travel

Still a further object of these inventions is to provide the abovedescribed capabilities within the context of an expert system that iscapable of learning individual prisoner/parolee behavior patterns andmaking notes of and/or generating alarm signals only when those patternsare violated in a suspicious manner, and further, that penalizesimproper behavior by restricting areas and periods of travel.

It is another object of these inventions to provide prisoner/paroleetracking, monitoring, warning, and learning systems and methods thatmake use of reinforcement learning whereby prisoners/parolees arerewarded for conformance to specified behavior patterns.

It is still a further object of these inventions to provideprisoner/parolee tracking, monitoring, warning, and learning systems andmethods that permit simultaneous tracking of a multitude ofprisoners/parolees from a prisoner/parolee control center.

Yet a further object of these inventions is to provide prisoner/paroleetracking, monitoring, warning, and learning systems and methods thatpermit dispatching of police or other law enforcement personnel whendangerous situations are detected that involve prisoners/parolees.

Still a further object of these inventions is to provideprisoner/parolee tracking, monitoring, warning, and learning systems andmethods that enable physical monitoring of prisoners/parolees including,for example, monitoring prisoner/parolee heart rate and chemicalcomposition of prisoner/parolee perspiration to detect excited oragitated states of prisoners/parolees or the presence of intoxicatingdrugs, alcohol or other substances within the prisoner's/parolee's bodysystem.

Still a further object of these inventions is to provideprisoner/parolee tracking, monitoring, warning, and learning systems andmethods that monitor sounds and audible signals generated by theprisoner/parolee or in the vicinity of the prisoner/parolee, and analyzethose sounds for detection of dangerous situations such as gunshots.

Yet a further object of these inventions is to provide prisoner/paroleetracking, monitoring, warning, and learning systems and methods thatpermit transmission of spoken commands from a prisoner/parolee controlcenter to individual prisoners/parolees directing them to return toprescribed locations or travel patterns, and to warn thoseprisoners/parolees that they have violated their predetermined movement,travel, or other conditions of parole.

Still a further object of these inventions is to provide warning to thegeneral population within areas that may be threatened by individualprisoners or parolees that have violated their prescribed travel spaceor behavior restrictions.

Yet another object of these inventions is to provide prisoner tracking,monitoring, warning, and learning systems and methods that make use ofprisoner sensor processing units that are securely attached to theprisoner in a manner that cannot be removed without generating warningsignals.

Further objects of the invention are apparent from reviewing the summaryof the invention, detailed description, and claims which are set forthbelow.

The above and other objects are achieved by a method of monitoring andlearning a subject's behavior. A first file including reference behaviordata defining several classes of individuals to be monitored is createdand stored in a memory of a monitoring station computer. Included withinthat file is data relating to at least one class to which the subjectbelongs. A second file including behavior data defining the subject tobe monitored is also created and stored in the monitoring stationcomputer memory. The monitoring station computer is defined andprogrammed with data defining a set of allowed activities for each ofthe several classes of individuals to be monitored. The monitoringstation computer is also defined and programmed with data defining a setof allowed activities that are specific for the subject to be monitored.These allowed activities include predefined routes and times of travelin a location remote from the monitoring station computer. A remotemonitoring transmitter and receiver is attached to the subject. Thereceiver cooparates with a satellite global positioning system todetermine the subject's current location as the subject moves about inthe area located remote from the monitoring station computer. Datadefining the subject's location at a specific time is periodicallytransmitted from the remote monitoring transmitter and receiver to themonitoring station computer. The data transmitted from the remotemonitoring transmitter is analyzed by the computer by comparing the datadefining the subject's current location and time with the set of allowedactivities that are specific for the subject. The computer determines ifthere are any variations from the allowed activities. A first alarmsignal is generated defining any determined variation from the allowedactivities. An expert system is used to further analyze the first alarmsignal defining the determined variation from the allowed activities.This expert system is programmed to recognize a continuum of degrees ofalarms based on a comparison of the determined variation, the behaviordata defining the subject to be monitored, and the reference behaviordata defining the class of individuals to which the subject belongs. Theexpert system also generates a second alarm signal defining a specificrecommended course of action that is appropriate for the determinedvariation, the subject's specific behavior data, and the data definingthe class of individuals to which the subject belongs. Hereafter, thephrase prisoners or parolees are used interchangeably and may just aseasily be used for children, incompetent persons, or the aged.

Furthermore, the data defining the subject's current location and time,the set of allowed activities that are specific for the subject, and thesecond alarm signal defining the recommended course of action are morefrequently analyzed. Based on this more frequent analysis, it isdetermined whether the second alarm condition has changed by becomingmore or less critical. If necessary, the second alarm condition ismodified to reflect any determined change.

The reference behavior data defining several classes of individuals tobe monitored includes but is not limited to criminal behavior data,criminal history and criminal record data, parole level information,data relating to a number of different types of crimes, data relating toa defined deviated behavior standard derived, and crime probability datathat compares various crime types with various location types wherein acrime probability for each of the various crime types is determined andassigned for each of the various location types. The behavior datadefining the subject to be monitored includes but is not limited tocriminal behavior data, criminal history and criminal record data,parole level information, and data relating to types of crimes committedby the subject. The data defining a set of allowed activities for eachof the several classes of individuals to be monitored includes but isnot limited to permitted travel data, permitted location data, permittedlocation dwell time data, and permitted travel path data.

Also, the remote monitoring transmitter and receiver has an audiblealarm. A signal is transmitted from the monitoring station computer tothe monitoring transmitter and receiver attached to the subject. Thesignal activates the audible alarm to indicate to the subject that analarm condition has been triggered. An expert system is operated toanalyze the first and second alarm conditions and the data thatgenerated the alarm conditions. The first and second data files aremodified to reflect learned activities that either should or should notgenerate an alarm. The expert system learns behavior that unnecessarilygenerated an alarm. The behavior data defining the subject to bemonitored and the data defining the set of allowed activities that arespecific for the subject to be monitored are accordingly modified sothat the alarm is not generated in the future.

Periodic monitoring of the subject's behavior continues. After apredefined period without an alarm being generated, the data definingthe set of allowed activities that are specific for the subject to bemonitored is modified to provide for an increased area and longerallowed time of travel. The increase area and longer time of travel isaccordingly communicated to the remote monitoring transmitter andreceiver attached to the subject. After an alarm is generated, the datadefining the set of allowed activities that are specific for the subjectto be monitored is modified to provide for a decreased area and shortertime of travel. The decreased area and shorter allowed time of travel isaccordingly communicated to the remote monitoring transmitter andreceiver attached to the subject.

The remote monitoring transmitter and receiver attached to the subjectis used to monitor a physical attribute of the subject. Data definingthe monitored physical attribute of the subject is transmitted from theremote monitoring transmitter and receiver to the monitoring stationcomputer. The computer analyzes the data by comparing the data definingthe subject's monitored location, time and physical attributes with theset of allowed activities that are specific for the subject. Thecomputer determines if there are any variations from these allowedactivities. The physical attributes being monitored include but are notlimited to speech, alcoholic levels, heart rate, breath andperspiration.

The above and other objects are also achieved by a system of monitoringand learning a subject's behavior. The system includes at least amonitoring station computer, a remote monitoring transmitter andreceiver, and an expert system. The monitoring station computer createsand stores in its memory a first file including reference behavior datadefining several classes of individuals to be monitored. Included withinthat file is data relating to at least one class to which the subjectbelongs. The monitoring station computer also creates and stores in itsmemory a second file including behavior data defining the subject to bemonitored is also created and stored in the monitoring station computermemory. The monitoring station computer is defined and programmed withdata defining a set of allowed activities for each of the severalclasses of individuals to be monitored. The monitoring station computeris also defined and programmed with data defining a set of allowedactivities that are specific for the subject to be monitored. Theseallowed activities include predefined routes and times of travel in alocation remote from the monitoring station computer. The remotemonitoring transmitter and receiver is attached to the subject. Thereceiver cooperates with a satellite global positioning system todetermine the subject's current location as the subject moves about inthe area located remote from the monitoring station computer. Datadefining the subject's location at a specific time is periodicallytransmitted from the remote monitoring transmitter and receiver to themonitoring station computer. The data transmitted from the remotemonitoring transmitter is analyzed by the monitoring computer bycomparing the data defining the subject's current location and time withthe set of allowed activities that are specific for the subject. Themonitoring computer determines if there are any variations from theallowed activities. A first alarm signal is generated by the monitoringcomputer defining any determined variation from the allowed activities.The expert system is used to further analyze the first alarm signaldefining the determined variation from the allowed activities. Thisexpert system is programmed to recognize a continuum of degrees ofalarms based on a comparison of the determined variation, the behaviordata defining the subject to be monitored, and the reference behaviordata defining the class of individuals to which the subject belongs. Theexpert system also generates a second alarm signal defining a specificrecommended course of action that is appropriate for the determinedvariation, the subject's specific behavior data and the data definingthe class of individuals to which the subject belongs.

BRIEF DESCRIPTION OF THE DRAWINGS

The inventions of this application are better understood in conjunctionwith the following drawings and detailed description of the preferredembodiments. The various hardware and software elements used to carryout the inventions are illustrated in the attached drawings in the formof block diagrams, flow charts, and other appropriate pictorialrepresentations.

FIG. 1 is a flow chart of the overall prisoner/parolee tracking,monitoring, and learning algorithm herein disclosed.

FIG. 2 is a block diagram of the overall hardware system for tracking,monitoring, and learning prisoner/parolee behavior herein disclosedshowing a hardware sub-system for prisoner/parolee behavior tracking andmonitoring and a hardware sub-system for prisoner/parolee behaviorlearning.

FIG. 3 is a graphical representation of the overall prisoner/paroleetracking, monitoring, and learning systems and methods herein disclosed.

FIG. 4 is a diagram illustrating the principal elements of theprisoner/parolee sensor or processor unit to be carried on the person ofthe prisoner or parolee.

FIG. 5 is a diagram illustrating the principal elements of theprisoner/parolee control center used to maintain databases and receiveinformation from prisoners or parolees being tracked and monitored.

FIG. 6 is an illustration of the prisoner/parolee sensor or processorunit including a drug detection device attached to a strap wherein thestrap attaches the device to the prisoner or parolee being tracked andmonitored.

FIG. 7 illustrates the attachment of a drug/substance detector to theprisoner/parolee drug detection device strap so that the detector is incontact with the skin of the prisoner/parolee being tracked andmonitored.

FIG. 8 illustrates a more detailed cross-section of the drug/substancedetector of FIG. 6.

FIG. 9 is a general block diagram of the learning prisoner/paroleebehavior aspect of the present invention.

FIG. 10 is a flow chart of a specific algorithm for learning individualprisoner/parolee behavior.

FIG. 11 is a modified version of flow chart of FIG. 10 including use ofan expert system (i.e. fuzzy logic, neural networks, reinforcementlearning, etc.).

FIG. 12 is a flow chart for determining a prisoner/parolee violation anda corresponding parole level for an individual prisoner/parolee.

FIG. 13 is a prisoner/parolee information chart which outlinesinformation assigned to prisoners/parolees and wherein prisoner/paroleeparole levels and constraints are assigned to prisoners/parolees.

FIG. 14 is a chart showing prisoner/parolee parole level definitions.

FIG. 15 is a flow chart of an expert system algorithm that is used fordetermining whether deviated behavior constitutes a crime or violation.

FIG. 16 is a flow chart of an algorithm for learning an aggregate numberof prisoners'/parolees' behavior wherein data on a congregate ofprisoners/parolees from the database are gathered and compiled andwherein the data are analyzed and sorted into various prisoner/paroleecategories.

FIG. 17 is a flow chart of an algorithm for learning an aggregate numberof prisoners'/parolees' behavior that further includes the act ofanalyzing statistical information and providing learning information ineach of the various prisoner/parolee categories using expert system(i.e. fuzzy logic, neural networks, reinforcement learning, etc.)algorithms for analyzing deviated behavior.

FIG. 18 is a block diagram of deviated behavior standard data thatdefines the deviated behavior standard for prisoners/parolees.

FIG. 19 is a directed graph illustrating permissible prisoner/paroleetravel between designated locations.

FIG. 20 is a ring/sector map useful in the disclosed prisoner/paroleetracking and monitoring system and method to define permissiblegeographic areas in which or through which the prisoner or parolee maytravel.

FIG. 21 illustrates a prisoner or parolee travel matrix corresponding tothe graph of FIG. 19 useful in defining approved destinations andlocations of the prisoner or parolee being tracked and monitored.

FIG. 22 is a travel time matrix showing expected travel times for theprisoner or parolee being tracked and monitored between the variouslocations indicated in FIG. 19.

FIG. 23 is a location dwell time matrix showing average or maximum times(or both) that a prisoner or parolee is permitted to spend at a givenlocation.

FIG. 24 is a ring/sector time matrix recording permissible average ormaximum (or both) time intervals that a prisoner or parolee may spend inindividual ring/sector segments of FIG. 22.

FIG. 25 is a prisoner or parolee monitor flow chart illustrating toplevel logical flow of the prisoner/parolee tracking and monitoringsystems and methods herein disclosed.

FIG. 26 is a general flow chart of the reinforcement learning algorithmof the prisoner/parolee behavior tracking or monitoring system.

FIG. 27 is a more specific flow chart of the reinforcement learningalgorithm of the prisoner/parolee behavior tracking or monitoringsystem.

FIG. 28 is a block diagram of the parole agents and sub-agents used forthe reinforcement learning algorithm of the prisoner/parolee behaviortracking or monitoring system.

FIG. 29 is a graph of location types against crime types for learningaggregate prisoner's/parolee's behavior algorithm.

The above figures are better understood in connection with the followingdetailed description of the preferred embodiments.

DETAILED DESCRIPTION

The present invention relates to prisoner/parolee tracking, monitoring,and learning system and corresponding methods in accordance with theherein disclosed inventions. FIG. 1 shows a general algorithm 10 for thepresent invention. The algorithm 10 starts at block 12. At block 14, thealgorithm 10 obtains prisoner/parolee data and monitoring data fromindividual prisoners/parolees. These data include but are not limited toreference behavior data that define several classes of individuals to bemonitored, behavior data defining the subject to be monitored, and datadefining a set of allowed activities for each of the several classes ofindividuals to be monitored. The algorithm 10 stores and updates theprisoner/parolee data and prisoner/parolee monitoring data at block 16.Algorithm 10 moves to block 18 where the algorithm 10 learns theprisoner/parolee behavior. At block 20, the prisoner/parolee data andmonitoring data in the database are updated. At block 22, the algorithm10 determines whether the prisoner/parolee 38 should be recommended tobe set free. If the prisoner/parolee 38 is not to be set free, thealgorithm 10 loops back to block 12 (in FIG. 1) where theprisoner/parolee data and monitoring data from individualprisoners/parolees 38(FIG. 3) are further obtained. If theprisoner/parolee 38 is to be set free, then the algorithm 10 executescommands to set the prisoner/parolee 38 free at block 24. At block 26,the prisoner/parolee records are retained for the prisoner/paroleehistory at block 26. The algorithm 10 ends at block 28. Algorithm 10shows that the present invention relates to an all-encompassing,broad-based system and algorithm for tracking, monitoring, learning, andreinforcing the behavior of the prisoners/parolees 38.

FIG. 2 shows an overall hardware system 30 for prisoner/parolee behaviortracking, monitoring, and learning. The overall hardware system 30includes a hardware sub-system 32 for prisoner/parolee behavior trackingand monitoring; and a hardware sub-system 34 for prisoner/paroleebehavior learning. The hardware sub-system 32 tracks and monitorsindividual prisoner/parolee behavior, and the hardware sub-system 34learns the behavior of prisoners/parolees individually and on anaggregate level.

FIG. 3 illustrates a prisoner/parolee tracking and monitoring system andmethod in accordance with the herein disclosed inventions. The systemand method of FIG. 3 permits simultaneous tracking and monitoring ofmultiple prisoners or parolees 38. The individual prisoners/parolees 38each have securely attached to their body a prisoner sensor/processorunit 52. The unit 52 may be attached to the arm, the leg, the waist orin any convenient manner to each prisoner/parolee 38. Such attachmentis, however, secured and may only be removed by an authorized person.The prisoner or parolee sensor/processor unit 52 permits tracking thelocation of individual prisoners/parolees 38 as well as monitoring audiosounds, communicating with the prisoner/parolee 38 or other personnel inthe vicinity of the prisoner/parolee 38, and monitoring physicalconditions of the prisoner/parolee 38 including, for example, his/herheart rate, pulse, blood pressure, respiration, temperature, andchemical properties of selected body fluids such as sweat and/or breath.The unit 52 is a compact electronic system specifically designed forcomprehensive monitoring, tracking, and learning of prisoners/parolees38.

As illustrated in FIG. 3, GPS satellites 36 are used to providecontinuous tracking and surveillance of the location and travel ofindividual prisoners/parolees 38. GPS tracking and ranging signals 37are received via the prisoner/parolee sensor or processing units 52which make use of known GPS calculation procedures to determine thelocation of individual prisoners or parolees 38. The individualprisoner/parolee sensor or processor units 52 receive signals frommultiple GPS satellites 36 and make use of known triangulationcalculation procedures and methods for determining prisoner/paroleelocations. Such location calculations make use of precisely timedranging signals using pseudo random (PRN) coding techniques in a knownmanner as described, for example, in the above referenced various GPSpatents which are herein incorporated by reference.

As illustrated in FIG. 3, the prisoner/parolee sensor or processor units52 are designed to communicate with control center 42 via radio controllinks 46. The control center 42 may query individual prisoners/parolees38 for their location and travel status including records of recenttravel time spent at particular locations. Parameters indicative of thetravel and time spent at particular locations of individual prisoners orparolees 38 are transmitted via radio control center links 46 to thecontrol 42. Additional parameters indicative of substance abuse, suchas, alcohol or drugs are also transmitted via the control links 46.Individual prisoners/parolees 38 are identified using uniqueidentification codes which may be used to address particular prisonersor parolees 38 via the radio control links 46.

The radio control links 46 may make use of radio communications,standard cellular telephone technology, or other two-way radiocommunication methods to communicate with the control center 42. Inaddition, as illustrated in FIG. 3, the prisoner/parolee sensor orprocessing units 52 may communicate through home base 40 via standardtelephone links 44 with the control center 42. The home base 40 wouldtypically be the principal place of residence of the prisoner or parolee38. The prisoner or parolee 38 may be required by legal authorities toperiodically transmit the outputs of the sensor or processing unit 52via the telephone link 44 to the control center 42. The telephonicconnection may supplement or replace the radio link 46 or may be used inaddition to those links 46 for periodic communication with controlcenter 42.

Also illustrated in FIG. 3 are police or security units 48 whichcommunicate via radio links 50 with the control center 42. Security orpolice personnel may be dispatched in units 48 to particular locationsto check on, monitor, or apprehend prisoners or parolees 38 dependingupon reported activities of those prisoners or parolees 38 through thesystems and methods of FIG. 3. In addition to being used fordispatching, the two-way radio links 50 may be used by security orpolice personnel to report status or information pertaining toindividual prisoners or parolees 38 being tracked and monitored by thesystems and methods of FIG. 3.

FIG. 4 illustrates in block diagram form a preferred embodiment of theprisoner/parolee sensor or processing unit 52 used in the inventionsherein disclosed. The unit 52 of FIG. 4 is to be worn and secured to thebody of the prisoner or parolee 38 to be tracked and monitored. Highspeed, VLSI sensor, processing, and radio transceiver electronics areused in the unit 52, resulting in a compact, lightweight portableprisoner/parolee sensor or processor unit 52 that may be comfortablyworn by the person to be tracked and monitored.

The unit 52 of FIG. 4 uses microprocessor control and routing circuitry51 to interconnect the various sensors, computing elements, and radiocommunication elements illustrated in the figure. The control androuting circuitry 51 includes appropriate high speed bus switchingand/or matrix switching circuitry of the type well known to thoseskilled in the art to interconnect the various electronic elements ofFIG. 4 under direction of the microprocessor controller included as partof unit 51.

The unit 51 also includes display and control interfaces attachedthrough interface circuit 110. The display 108 may be a small LCDintegrated circuit as a part of the physical sensor/processor unit 52,or may, in fact, be an external display attached to the unit 52 throughan appropriate communication port in the unit. The display 108 iscoupled to the unit 51 via interface circuit 110. The control keyboard106 of FIG. 4 may likewise be individual control buttons and keys thatare a physical part of the prisoner or parolee sensor/processor unit 52or may be a more complete keyboard attached to the unit through anappropriate control interface port. The keyboard 106 is also coupled tothe unit 51 via interface circuit 110. The keyboard/control unit 106,display 108, and interface 110 may be used in the initial setup of theprisoner/parolee sensor or processing unit 52 to specify particulartravel and prisoner or parolee monitor parameters useful in tracking andmonitoring the prisoner or parolee 38.

FIG. 4 also illustrates a telephonic communication port 104 for couplingof the prisoner/parolee sensor or processor unit 52 to standardtelephone lines for communication with a remote control center 42. Thetelephonic communication port 104 may be used both for loading controland monitor parameters into the memory units of the prisoner/paroleesensor or processor unit 52 and for querying the unit 52 for the purposeof obtaining prisoner or parolee travel and monitoring parameterscollected by unit 52 for use at the remote prisoner or parolee trackingand monitoring center 42 of FIG. 3. In addition to the telephonic port104, the radio transceiver 96 with its associated antenna 97 may be usedfor communication with the remote prisoner or parolee tracking andmonitoring control center 42 of FIG. 3. The radio transceiver 96 maylikewise be used for both setting of control and monitor parameters inthe unit 52 and for querying unit 52 for tracking and monitoringresulting data.

The microprocessor and control routing circuitry 51 is attached to readonly memory (ROM) 54 for permanent storage of control programs and/ordata. The unit 52 also includes random access memory (RAM) 58 useful inexecution of programs and collection of data by the prisoner/paroleesensor or processor unit 52. The clock unit 56 provides time referencesand time stamp data to be recorded with prisoner or parolee travel andmonitoring parameters. The clock 56 is useful for indicating a time ofoccurrence of particular events, the duration of events such as traveltimes, and the time that a particular prisoner or parolee 38 spends ordwells at a particular location. Such parameters are useful incontrolling the travel of the prisoner or parolee 38 in the mannerdescribed below.

The prisoner/parolee sensor or processor unit 52 of FIG. 4 also includesa battery 98 for powering of the unit 52 and its associated circuitry. Asensor 99 is used to monitor the charge remaining on the battery 98 andis coupled through analog to digital converter 100 to the microprocessorcontrol and routing circuitry 51. The sensor 99 enables a person todetermine when the battery unit 98 must be replaced or recharged. Theoutput of the sensor 99 may be communicated via the radio transceiver 96or telephone communications port 104 to the central control center 42 atprescribed intervals to indicate the status of the battery charge forindividual parolees or prisoners 38. In addition, a battery charger 102is included which is used to recharge the battery 98 under instructionsfrom the parolee or prisoner 38 or on command from the central controlcenter 42.

The prisoner/parolee sensor or processor unit 52 also includes anidentifier code generator 112 used to generate unique identificationcodes for individual prisoners or parolees 38 being tracked andmonitored. The specific identification code is appended tocommunications between the prisoner/parolee sensor or processor unit 52and the central control center 42 to identify and associate allcommunications and data files between the unit 52 and the centralcontrol center 42. The identifier code generator 112 is also used toaddress individual prisoner/parolee sensor or processor units 52 fromthe central control center 42, permitting selective polling ofindividual prisoner or parolee units 52 for the purpose of changingcontrol and monitor parameters within the unit 52 for querying the unit52 for location, travel, and monitor parameters.

The prisoner/parolee sensor or processor unit 52 of FIG. 4 includes GPSreceiver 60 coupled to GPS computer 62. The receiver 60 and computer 62are used to receive signals from multiple GPS satellites 36 forcalculation of the location of the prisoner or parolee 38 being trackedby the unit 52. The receiver 60 receives signals from multiple GPSsatellites 36 via antenna 61 as shown in FIGS. 3 and 4, withtransmission of received GPS ranging codes to GPS computer 62 foranalysis. GPS computer 62 makes use of standard and known triangulationcalculation methods to precisely determine the location of the prisoneror parolee 38 being tracked. The outputs of GPS computer 62 are used toreport the location of the prisoner or parolee 38 being tracked and alsoto report the route of travel of that prisoner or parolee 38 atspecified time intervals or at particular times under command of thelocation analysis computer 88.

Coordinate locations from the GPS computer 62 are fed to the locationanalysis computer 88 for further analysis and recording of prisoner orparolee location and travel routes. The location analysis computer 88 isused to compare the location and travel route coordinates and times topre-stored, permitted travel routes and location dwell times to insurethat the prisoner or parolee 38 is only traveling to permitted locationsat specified times and staying at those locations for allowed periods.In addition to comparison to pre-stored time and travel parameters, thelocation analysis computer 88 may implement expert system algorithmsdesigned to learn travel and dwell times of a given prisoner or parolee38 in determining deviations of prisoner/parolee behavior from normal orexpected individual travel and dwell time parameters. For example, usingthe location analysis computer 88, the prisoner/parolee sensor orprocessor unit 52 may "learn" prisoner or parolee behavior patternsuseful in tracking and monitoring individual prisoner or parolees 38.Such expected behavior patterns are useful in generating notations orwarning/control signals to be used in a manner further described below.

The prisoner/parolee sensor or processor unit 52 also includes a speaker68 attached to the unit 51 via digital to analog converter 66 fortransmission of commands or inquiries to the prisoner or parolee 38being tracked. The speaker 68 may also be used to sound audible alarmsto those in the vicinity of the prisoner or parolee 38 that may bedeemed to be in danger or in a situation wherein they should be madeaware of the presence of the prisoner or parolee 38 being tracked andmonitored. In addition to the speaker 68, a sensor microphone 64 coupledto the unit 51 via analog to digital converter 70 is used to permitaudio feedback from the prisoner or parolee 38 and also to sense audiosignals in the vicinity of the prisoner or parolee 38. The sensormicrophone 64 may be useful, for example, in hearing gunshots, shouting,fighting, or other audible signals indicative of violent behavior. Thesensor microphone 64 may also be used to record conversations betweenthe prisoner/parolee 38 and other persons. Such recording is useful, forexample, in assuring that the prisoner/parolee 38 does not engage inunauthorized activity such as drug dealing, plotting of criminalactivities, or other prohibited activities.

A voice microphone 72 coupled to unit 51 via analog to digital converter74 is also provided to further allow communication with the prisoner orparolee 38 or with other persons such as law enforcement agents orpersons that may be in danger because of the presence of the prisoner orparolee 38. For example, police personnel may use the voice microphone72 for direct communications with the control center 42 to indicateapprehension of a given prisoner or parolee 38. Circumstances of suchapprehension may also be transmitted via voice microphone 72, permittingdispatch of additional police personnel or other assistance as may beappropriate for a given situation. Audio inputs from the sensormicrophone 64 and voice microphone 72 may be analyzed in the audiosignal analysis unit 92 of FIG. 4. Such analysis permits automaticspeaker recognition to be certain that the person speaking or wearingthe prisoner/parolee sensor or processor unit 52 is indeed the prisoneror parolee 38 being tracked and monitored. Audio signal analysis unit 92may also be used to recognize spoken commands or responses from theprisoner or parolee 38 and to recognize particular sounds such asgunshots, shouting, fighting, etc. Depending on sound level sensitivity,the microphones 64 and 72 may be integrated with appropriate circuitryinto a single microphone unit.

Also illustrated in FIG. 4 are heart monitor 80 and sweat sensor 76coupled to unit 51 respectively through analog to digital converters 82and 78. These physical monitors permit select sensing of the prisoner orparolee physical parameters useful in the tracking and warning systemherein disclosed. For example, the heart monitor 80 may be used toindicate an excited state or condition that may be associated with crimeor other unauthorized activity. Sweat sensor 76 may be used to detectthe presence of drugs in the manner to be described below, permittingthe monitoring of substance abuse by the prisoner or parolee 38.Combining such monitoring with precise location information asdetermined via GPS receiver 60 and GPS computer 62 permits dispatchingof police or other personnel to the location of a particular prisoner orparolee 38 that may be engaged in illegal or unauthorized activity suchas the use of drugs, alcohol, etc. In addition to the heart monitor 80and sweat sensor 76 illustrated in FIG. 4, other physiological bodyparameter monitoring may be implemented including, for example, the useof breath analyzers that are on command from the central control center42 to determine the presence of alcohol.

The prisoner/parolee sensor or processor unit 52 illustrated in FIG. 4includes a break sensor 84 with an alarm code generator 86 used toreport any attempt by the prisoner or parolee 38 to remove theprisoner/parolee sensor or processor unit 52 from his or her body. Theunit 52 is attached to the prisoner or parolee 38 with a strap. Thebreak sensor 84 is configured to permit sensing of removal of the unit52 by, for example, cutting of the strap or otherwise interrupting thephysical integrity of the attachment mechanism to the prisoner orparolee 38 being tracked and monitored.

Communication processor 94 of FIG. 4 is used to coordinatecommunications with the central control and monitoring station 42 viathe telephone port 104 and/or radio transceiver 96. Communicationprocessor 94 both receives messages from the central control center 42and formats and transmits messages to that center 42. Communicationprocessor 94 may be used to coordinate both voice and datacommunications using the various computer and sensor devices describedabove. In addition, the communication processor 94 has access toidentification code generator 112 and clock 56 via microprocessorcontrol and routing circuitry 51 for appending identification codes andtime stamps to messages transmitted to the remote control center 42.

FIG. 5 illustrates the configuration of the prisoner or parolee controlcenter 42 of FIG. 3. As illustrated in FIG. 3, the prisoner/paroleecontrol center 42 is used to communicate with the individual prisonersor parolees 38 as well as with police and security personnel andvehicles 48 via the indicated radio control links 50. In addition,telephonic couplers 44 may be coupled to the control center 42 from thehomebase of individual prisoners or parolees 38. As shown in FIG. 5, theprisoner/parolee control center 42 includes a telephone line interface122 coupled to telephonic coupler 44. In addition, a radio transceiver114 is used for communication with individual prisoners or parolees 38via radio control links 46 and 50. The telephonic interface 122 andradio transceiver interface 114 are coupled to the local prisoner orparolee control center computer and control equipment of the controlcenter 42 via the interface bus or switching mechanism 113. In additionto the telephonic voice line interface 122, the control center 42permits data communication via telephone modem 120 also interfaced withswitching bus 113 as shown in FIG. 5. Audio interface 125 permits directcommunication with prisoner or parolee control center operatingpersonnel via microphone 129 and earphones 127. This configurationenables operating personnel to communicate directly with police andsecurity personnel via radio transceiver 114 and radio links 50 and alsoto communicate with parolees or personnel via the telephone interface 44or radio links 46 as illustrated in FIGS. 3 and 5. In addition, theaudio interface 125 permits prisoner or parolee control center operatingpersonnel to "listen in" via sensor microphone 64 and/or voicemicrophone 72 of FIG. 4 to sounds and/or to speakers in the vicinity ofindividual prisoners or parolees 38 for the purpose of hearingconversations or other audible sounds such as gunshots, shouting,fighting, etc. that may indicate an emergency or dangerous situation. Asindicated in FIG. 5, a voice recorder unit 132 is also provided as partof the prisoner or parolee control center 42 and is interfaced toswitching control bus 113 via telephone receiver 130. The voice recorder132 may be used to record individual voice messages and also to providevoice response messages such as audible messages to security personnelin response to inquiries from such personnel via radio links 50 of FIG.3.

Prisoner or parolee control center operating personnel interface to thecontrol center communication and computing apparatus via the keyboard124, display 126, and printer 128. These interfaces, together with theabove describe audio interfaces, provide prisoner or parolee controlcenter operating personnel complete access to voice and datacommunications to and from prisoners or parolees 38 and to and fromsecurity personnel using the techniques and methods of FIGS. 3 and 4described above.

Also shown in FIG. 5 is the prisoner or parolee control center controlcomputer 116 and its associated data storage 118. The control computer116 is used to collect data from individual prisoners/parolees 38indicative of their locations, travels, and the time intervalsassociated with such travels, or periods spent at individual locations.The computer control center 116 is able to compare such data toauthorized travel and time data for each individual prisoner or parolee38. Based on these comparisons, warning signals may be generated toalert prisoner or parolee control center operators of violations ofauthorized movements by the prisoner/parolee 38. Indications ofsubstance abuse received from individual prisoners/parolees 38 via theapparatus and methods described above are also collected via controlcomputer 116 for storage in data storage unit 118 with subsequentindication to prisoner or parolee control center operating personnel ofprisoner or parolee violations so that appropriate action may be taken.Additionally, the prisoner or parolee control center control computer116 accesses prisoner or parolee data and prisoner or parolee monitoringdata, and the computer 116 learns the prisoners' or parolees' behaviorsfrom the data.

FIG. 6 is a pictorial representation of the prisoner/parolee sensor orprocessor unit 52 of FIG. 4 showing a display 108 and keyboard/controlbuttons 106. The various communication ports are indicated on the sideof unit 52. In addition, strap 134 is illustrated in FIG. 6 and is usedto attach the prisoner/parolee sensor or processor unit 52 to an arm,leg, or the torso of the prisoner or parolee 38 being tracked andmonitored. The strap 134 includes a drug or substance abuse detector 140which makes direct contact with the skin of the prisoner/parolee sensoror processor unit 52 via wire 138. The strap 134 is designed such thatany attempt to disconnect or remove the prisoner/parolee sensor orprocessor unit 52 from the prisoner or parolee 38 will result in analarm signal generated by break sensor 84 and alarm code generator 86shown in FIG. 4 and discussed above. Upon activation of the break sensor84, a tranquilizing substance may be automatically injected into theprisoner/parolee 38 to give authorities time to apprehend theprisoner/parolee 38 and inspect, repair, or replace the damagedprisoner/parolee sensor or processor unit 52.

FIG. 7 illustrates the sensor 140 in contact with the skin 141 of theprisoner or parolee 38. The strap 134 attaches the detector 140 to thesensor/processor unit 52 as illustrated in FIG. 6. The sensor 140 is ofa type known in the art that is worn in a manner so that it is in directcontact with the skin 141 as illustrated in FIGS. 7 and 8. The sensor140 permits detection of such substances as methampheamine, morphine,tetrahydrocannabinol (THC) and cocaine. The sensor 140 is responsive tothe presence of chemicals indicative of the substances of the sweat orperspiration of the person 38 wearing the detector. Such known devicesare called Remote Biochemical Assay Telemetering System (R-BATS), or"drug badges" and are conceived for monitoring convicted drug users onprobation. A drug badge of this type is described, for example, in theMay 1996 issue of NASA Technical briefs in an article entitled "DevicesWould Detect Drugs in Sweat", incorporated herein by reference. Thedevice described in that article comprise three principal components asindicated in FIG. 8. The bar biochemical detector 142 absorbs sweat fromthe wearer (i.e. prisoner/parolee 38). The sweat is filtered through amembrane that will pass only smaller molecules such as those of drugs,water, or salt. The optical detector 144 uses photoelectric sensors.Florescent labels are attached to the drug molecules and are irradiatedresulting in florescence that are able to be sensed by the optical orphoto detector 144. The transmitter 146 of FIG. 8 transmits anindication of such molecules to the prisoner/parolee sensor orprocessing unit 52 via wire 138 as indicated in FIGS. 6 and 8 above.Using this detection technology in connection with GPS system, thetracking system and method herein disclosed enables not only notifyingofficials of substance abuse, but also providing location informationpermitting apprehension of the abuser.

Referring to FIG. 1, the general algorithm or method 10 comprises block14 with the operation of obtaining prisoner or parolee data andmonitoring data from individual prisoners/parolees 38, block 16 with theact of storing prisoner or parolee data and monitoring data into adatabase, block 18 with the act of learning prisoner or paroleebehavior, and block 20 with the act of updating the prisoner or paroleedata and monitoring data in the database. The learning prisoner orparolee behavior block 18 is divided into two further general blocks:the learning individual prisoner/parolee behavior block 150 and thelearning aggregate prisoner/parolee behavior block 152 as shown in FIG.9.

More specifically as to the general implementation, the method 10involves monitoring and learning a subject's behavior. This method 10includes creating and storing in a memory of a monitoring stationcomputer 116 a first file including reference behavior data definingseveral classes of individuals 38 to be monitored, including at leastone class to which the subject 38 belongs. The method 10 also involvescreating and storing in the monitoring station computer memory 118 asecond file including behavior data defining the subject 38 to bemonitored, and the method 10 defines and programs the monitoring stationcomputer 116 with data defining a set of allowed activities for each ofthe several classes of individuals 38 to be monitored. The monitoringstation computer 116 is defined and programmed with data defining a setof allowed activities that are specific for the subject 38 to bemonitored, wherein the allowed activities include predefined routes andtimes of travel in a location remote from the monitoring stationcomputer 116.

A remote monitoring transmitter and receiver 52 is attached to thesubject 38, wherein the receiver cooperates with a satellite globalpositioning system 36 to determine the subject's current location as thesubject 38 moves about in the area located remote from the monitoringstation computer 116. Data defining the subject's location at a specifictime is periodically transmitted from the remote monitoring transmitterand receiver 52 to the monitoring station computer 116. The datatransmitted from the remote monitoring transmitter is computer analyzedby comparing the data defining the subject's current location and timewith the set of allowed activities that are specific for the subject 38and determining if there are any variations from the allowed activities.A first alarm signal defining any determined variation from the allowedactivities is generated, and the first alarm signal defining thedetermined variation from the allowed activities is further analyzedusing an expert system programmed to recognize a continuum of degrees ofalarms based on a comparison of the determined variation, the behaviordata defining the subject to be monitored, and the reference behaviordata defining the class of individuals to which the subject 38 belongs.A second alarm signal defining a specific recommended course of actionthat is appropriate for the determined variation, the subject's specificbehavior data, and the data defining the class of individuals to whichthe subject belongs, is generated from the expert system.

A number of algorithms or methods for learning individualprisoner/parolee behavior exist, and any suitable algorithm may be usedin conjunction with the present invention. FIG. 10 shows a generalexample algorithm 154 for learning individual prisoner/parolee behavior.Algorithm 154 starts at block 156. At block 158, the algorithm 154accesses prisoner/parolee data and prisoner/parolee monitoring data fromthe database. The algorithm 154 moves to block 160 where the normalbehavior standard for individual prisoners/parolees 38 is accessed fromthe database and the monitored prisoner/parolee data is compared withthe normal behavior standard.

At block 162, the algorithm 154 determines whether the monitoredprisoner/parolee data deviates from normal behavior. If there is adeviation in behavior at block 162, then the deviated behavior is notedand/or appropriate action is taken at block 164. The algorithm 154 movesto decision block 166 where it is determined whether the normal behaviorstandard for the prisoner/parolee needs to be changed or updated. If thenormal behavior standard needs to be changed, then the algorithm 154moves to block 168 where the normal behavior standard in the database isupdated and changed and then loops back to block 158 where theprisoner/parolee data and monitoring data from the database is accessed.If the normal behavior standard does not need to be changed, then thealgorithm 154 loops directly back to block 158 where theprisoner/parolee data and monitoring data from the database is accessed.

If there is no deviation in behavior at block 162, then the algorithm154 moves to decision block 170 where it is determined whether theprisoner/parolee 38 should be set free. If the prisoner/parolee 38 isnot to be set free, then the algorithm 154 loops back to block 158 wherethe prisoner/parolee data and monitoring data from the database is againaccessed. If the prisoner/parolee 38 is to be set free, the algorithm154 moves to block 172 where the prisoner/parolee is set free and toblock 174 where the algorithm 154 retains the prisoner/parolee recordsfor each prisoner's/parolee's criminal record or history. The algorithm154 ends at block 176.

FIG. 11 shows an example algorithm 178 for learning individualprisoner/parolee behavior wherein the algorithm 178 uses an expertsystem(s). The algorithm 178 starts at block 180. At block 182, thealgorithm 178 accesses prisoner/parolee data and prisoner/paroleemonitoring data from the database. The algorithm 178 moves to block 184where the normal behavior standard for individual prisoners/parolees isaccessed from the database and the monitored prisoner/parolee data iscompared with the normal behavior standard.

At block 186, the algorithm 178 determines whether the monitoredprisoner/parolee data deviated from normal behavior. If there is adeviation in behavior at block 186, then the deviated behavior is notedor appropriate action is taken at block 188. The algorithm 178 moves toblock 190 where an expert system (i.e. including but not limited tofuzzy logic, neural networks, reinforcement learning, etc.) algorithm(s)is executed for analyzing the deviated behavior. The algorithm 178 movesto decision block 192 where it is determined whether the normal behaviorstandard for the prisoner/parolee needs to be changed or updated. If thenormal behavior standard needs to be changed or updated, then thealgorithm 178 moves to block 194 where the normal behavior standard inthe database is updated and changed, and the algorithm 178 then proceedsback to block 182 where the prisoner/parolee data and monitoring datafrom the database is accessed. If the normal behavior standard does notneed to be changed or updated, then the algorithm 178 loops directlyback to block 182 where the prisoner/parolee data and monitoring datafrom the database is accessed.

If there is no deviation in behavior at block 186, then the algorithm178 moves to decision block 196 where it is determined whether theprisoner/parolee 38 should be recommended to be set free. If theprisoner/parolee 38 is not to be set free, then the algorithm 178 loopsback to block 182 where the prisoner/parolee data and monitoring datafrom the database is again accessed. If the prisoner/parolee 38 is to beset free, the algorithm 178 moves to block 198 where theprisoner/parolee 38 is set free and to block 200 where the algorithm 178retains the prisoner/parolee records for each prisoner's/parolee'scriminal record or history. The algorithm 178 ends at block 202.

The above algorithm illustrates that the behavior data defining thesubject 38 to be monitored and learned includes but is not limited tocriminal behavior data, criminal history and criminal record data,parole level information, and data relating to types of crimes committedby the subject 38.

FIGS. 12-14 illustrate a parole level example for learning individualprisoner/parolee behavior. FIG. 12 shows th e algorithm 204 for theparole level example. The algorithm 204 starts at block 206. Thealgorithm 204 moves to block 208 where the prisoner/parolee data andprisoner/parolee monitoring data is accessed from the database. FIG. 13shows a chart 226 with prisoner/parolee data and prisoner/paroleemonitoring data assigned to various prisoners/parolee that are stored inthe database. The data includes but is not limited to theprisoner/parolee identification 228, the prisoner's/parolee's background230, criminal history and record 232, prisoner/parolee constraints 234(i.e. further including but not limited to allowed locations 236,prohibited locations 238, paths of travel 240, dwell times for variouslocations 242, etc.), current parole level 244, monitoring data 246(i.e. including but not limited to physical and physiologicalparameters, current location information, etc.). The database stores andholds all of the prisoner/parolee data and prisoner/parolee monitoringdata in the database for each prisoner/parolee (i.e. prisoner/parolee 1,prisoner/parolee 2, prisoner/parolee 3, etc.) as shown in FIG. 13. Thealgorithm 204 then moves to decision block 210 where it is determinedwhether the prisoner/parolee should be set free.

If the prisoner/parolee is to be set free, then the algorithm 204 movesto block 220 where the prisoner/parolee is set free. The algorithm 204moves to block 222 where the algorithm 204 retains the prisoner/paroleerecords for each prisoner's/parolee's criminal record or history. Thealgorithm 204 then ends at block 224.

If the prisoner/parolee 38 is not to be set free, then the algorithm 204moves to decision block 212 where it is determined whether aprisoner/parolee parole violation has occurred. If a violation has notoccurred, then the algorithm 204 moves to decision block 214 where it isdetermined whether the prisoner/parolee 38 is ready to be moved up aparole level. If the prisoner/parolee 38 is not ready to be moved up aparole level, then the algorithm 204 loops back to block 208 where theprisoner/parolee data and monitoring data from the database is againaccessed. On the other hand, if the prisoner/parolee 38 is ready to bemoved up a parole level, then the algorithm 204 moves to block 216 wherethe database is updated and stored with the prisoner's/parolee's newparole level. The algorithm 204 then loops back to block 208 where theprisoner/parolee data and prisoner/parolee monitoring data is accessedfrom the database.

The parole levels referred to above may be defined in many differentways. Examples of general definitions of various parole levels 249 areshown in the chart 248 of FIG. 14. FIG. 14 shows the parole levels 249from solitary confinement to freedom. FIG. 14 defines the various parolelevels as follows: 1) The zero (0) level of parole 250 is defined as"solitary confinement"; 2) The first(1^(st)) level of parole 252 isdefined as "maximum security prison"; 3) The second(2^(nd)) level ofparole 254 is defined as "minimum security prison"; 4) The third(3^(rd))level of parole 256 is defined as "stay at home outside of prison"; 5)The fourth(4^(th)) level of parole 258 is defined as "parole" whereinthe prison is allowed only to roam to established locations forestablished times."; 6) The fifth(5^(th)) level of parole 260 is definedas "parole" wherein there are no geographic constraints; 7) Thesixth(6^(th)) level of parole 262 is defined as "freedom/regularcitizen" wherein there are no restrictions at all.

Returning to the algorithm 204, at block 212, if a violation hasoccurred, then the algorithm 204 moves to block 218 where it isdetermined whether the prisoner/parolee 38 needs to be moved down aparole level. If the prisoner/parolee status does not need to be moveddown a parole level, then the algorithm 204 loops back to block 208where the prisoner/parolee data and monitoring data from the database isagain accessed. However, if the prisoner/parolee status needs to bemoved down a parole level, then the algorithm 204 moves to block 216where the database is updated and stored with prisoner's/parolee's newparole level. The algorithm 204 then loops back to block 208 where theprisoner/parolee data and prisoner/parolee monitoring data is accessedfrom the database.

In the general example of implementation, the method and system ofmonitoring and learning a subject's behavior further includes morefrequently analyzing the data defining the subject's current locationand time, the set of allowed activities that are specific for thesubject and the second alarm signal defining the recommended course ofaction, determining whether the second alarm condition has changed bybecoming more or less critical, and if necessary, modifying the secondalarm condition to reflect any determined change. The method and systemfurther comprises the act of updating the data defining the allowedactivities that are specific to the subject to include data defining thebehavior of the monitored subject that caused the issuance of the alarm.Also, the method and system further comprises the act of when the secondalarm has been modified by becoming less critical, modifying the datadefining the allowed behavior of the monitored subject to define theactivity.

Also, the remote monitoring transmitter and receiver has an audiblealarm, and a signal is transmitted from the monitoring station computerto the monitoring transmitter and receiver attached to the subject,which signal activates the audible alarm to indicate to the subject thatan alarm condition has been triggered. An expert system is operated toanalyze the first and second alarm conditions and the data thatgenerated the alarm conditions, and the first and second data files aremodified to reflect learned activities that either should or should notgenerate an alarm. The expert system learns behavior that unnecessarilygenerated an alarm. The behavior data defining the subject to bemonitored and the data defining the set of allowed activities that arespecific for the subject to be monitored are accordingly modified sothat the alarm is not generated in the future.

The method and system of monitoring and learning a subject's behaviorfurther include the acts of and the implementation of systems forcontinuing to periodically monitor the subject's behavior, and after apredefined period without an alarm being generated, modifying the datadefining the set of allowed activities that are specific for the subjectto be monitored to provide for an increased area and longer allowed timeof travel. The increase area and longer time of travel is accordinglycommunicated to the remote monitoring transmitter and receiver attachedto the subject. Also, the method and system of monitoring and learning asubject's behavior further include the acts of and the implementation ofsystems for continuing to periodically monitor the subject's behavior,and after an alarm is generated, modifying the data defining the set ofallowed activities that are specific for the subject to be monitored toprovide for a decreased area and shorter time of travel. The decreasedarea and shorter allowed time of travel is accordingly communicated tothe remote monitoring transmitter and receiver attached to the subject.

The method and system of monitoring and learning a subject's behaviorfurther include the acts of and implementation of systems for using theremote monitoring transmitter and receiver attached to the subject tomonitor a physical attribute of the subject, transmitting data definingthe monitored physical attribute of the subject from the remotemonitoring transmitter and receiver to the monitoring station computer,and wherein computer analyzing the data includes comparing the datadefining the subject's monitored location, time and physical attributeswith the set of allowed activities that are specific for the subject anddetermining if there are any variations from the allowed activities. Thephysical attributes being monitored include but are not limited tospeech, alcoholic levels, heart rate, breath and perspiration. Theremote monitoring transmitter and receiver 52 includes an audible alarm,and the method and system further comprises the act of transmitting asignal from the monitoring station computer 116 to the monitoringtransmitter and receiver 52 attached to the subject 38, which signalactivates the audible alarm to indicate to the subject 38 that an alarmcondition has been triggered.

FIG. 15 shows a learning individual prisoner behavior algorithm 264 thatuses an expert system and a criminology model. The algorithm 264 startsat block 266. The algorithm 264 moves to block 268 where the deviatedbehavior is inputted into the expert system (i.e. including but notlimited to fuzzy logic, neural networks, reinforcement learning, etc.).The algorithm 264 moves to block 270 where the deviated behavior isanalyzed using expert system algorithms by comparing deviated behaviorwith the criminology behavior.

At decision block 272, it is determined whether the deviated behaviorconstitutes a crime or violation based on the comparison. If thedeviated behavior does not constitute a crime or violation, then thedeviated behavior is made note of in the database at block 276, and thealgorithm 272 ends at block 278 via connector A 277. If the deviatedbehavior does constitute a crime or violation, then algorithm 264 sendsnotice or notices to take appropriate action(s) at block 274. Thealgorithm 264 then ends at block 278.

A number of algorithms or methods for learning aggregateprisoner/parolee behavior exist, and any suitable algorithm may be usedin conjunction with the present invention.

FIG. 16 shows a general example algorithm 280 for learning aggregateprisoner or parolee behavior. Algorithm 280 starts at block 282. Atblock 284, the algorithm 280 accesses prisoner or parolee data andprisoner or parolee monitoring data from the database. The algorithm 280moves to block 286 where the data on a congregate of prisoners/parolees38 from the database is gathered and compiled. At block 288, theprisoner/parolee data and monitoring data from the database areseparated into various prisoner/parolee categories. The algorithm 280then moves to block 290 where statistical information for each of thevarious prisoner/parolee categories is compiled. The algorithm 280 goesto block 292 where the statistical information is analyzed and learninginformation in each of the various prisoner/parolee categories isprovided. At decision block 294, the algorithm 280 determines whetherthe system is finished learning aggregate prisoner's/parolee's behavior.If the system is not finished with learning aggregateprisoner's/parolee's behavior, then the algorithm 280 loops back toblock 284 where the prisoner/parolee data and prisoner/paroleemonitoring data from the database is accessed. If the system is finishedwith learning aggregate prisoner's/parolee's behavior, then thealgorithm 280 moves to block 296 where the algorithm 280 ends.

FIG. 17 shows an example algorithm 298 for learning aggregateprisoner/parolee behavior wherein the algorithm 298 uses an expertsystem(s). The algorithm 298 starts at block 300. At block 302, thealgorithm 298 accesses prisoner or parolee data and prisoner or paroleemonitoring data from the database. The algorithm 298 moves to block 304where the data on a congregate/aggregate of prisoners or parolees 38from the database is gathered and compiled. At block 306, theprisoner/parolee data and monitoring data from the database areseparated into various prisoner/parolee categories. The algorithm 298then moves to block 308 where statistical information for each of thevarious prisoner/parolee categories is compiled. The algorithm 298 goesto block 310 wherein using expert system(i.e. fuzzy logic, neuralnetworks, reinforcement learning, etc.) algorithms for analyzingdeviated behavior, statistical information is analyzed and learninginformation in each of the various prisoner/parolee categories isprovided. At decision block 312, the algorithm 298 determines whetherthe system is finished learning aggregate prisoner's/parolee's behavior.If the system is not yet finished with learning aggregateprisoner's/parolee's behavior, then the algorithm 298 loops back toblock 302 where the prisoner/parolee data and prisoner/paroleemonitoring data from the database is accessed. If the system is finishedwith learning aggregate prisoner's/parolee's behavior, then thealgorithm 298 moves to block 314 where the algorithm 298 ends.

The data for learning aggregate prisoner/parolee behavior algorithms 280and 298 that determines the standard for deviated behavior is able to bedefined by various sources and models. FIG. 18 shows a general chart 316for the deviated behavior standard data. The general chart 316 haswithin in it various other sub-blocks. The general chart 316 includesbut is not limited to criminology model block 318; aprisoner's/parolee's rules, regulations, restrictions block 320; a legalstandards(Definition of Crimes/Violations) block 322; aprisoner's/parolee's normal behavior block 324; a prisoner's orparolee's behavior/exceptions/leeway's block 326; prisoner's/parolee'snon-responsiveness/lost tracking block 328; and other prisoner/paroleerelevant data or information block 330.

The above algorithms illustrate that the reference behavior datadefining several classes of individuals 38 to be monitored includes butis not limited to criminal behavior data, criminal history and criminalrecord data, parole level information, data relating to a number ofdifferent types of crimes, data relating to a defined deviated behaviorstandard derived, and crime probability data that compares various crimetypes with various location types wherein a crime probability for eachof the various crime types is determined and assigned for each of thevarious location types.

In the general example of implementation, the method and system includesthe acts of operating an expert system to analyze the first and secondalarm conditions and the data that generated the alarm conditions andmodifying the first and second data files to reflect learned activitiesthat either should or should not generate an alarm. In operating theexpert system, the method and system further involve the acts of using acriminology model to which the determined variation from the allowedactivities is compared, determining whether the variation from theallowed activities constitutes a crime based on the comparison,recording the variation from the allowed activities into the second fileof the monitoring station computer memory 118, and recommending anynecessary appropriate action responsive to the determination of thevariation from the allowed activities.

The method and system also involves the acts of operating an expertsystem to learn behavior that unnecessarily generated an alarm andmodifying the behavior data defining the subject to be monitored.Furthermore, the method and system further involves the acts ofoperating an expert system to learn behavior that unnecessarilygenerated an alarm and modifying the data defining the set of allowedactivities that are specific for the subject to be monitored so that thealarm is not generated in the future.

The following example illustrates the defining of constraints, rules,regulations, and restrictions for prisoners/parolees 38, and thetracking, monitoring, and learning of deviated behavior from theconstraints for the prisoners/parolees 38.

Turning now to the tracking of prisoner/parolee movements, FIG. 19presents an example digraph illustrating the permitted travel of anindividual prisoner or parolee 38. Location A 334 is the home base orplace of residence of the prisoner/parolee 38. The prisoner/parolee 38in this example is authorized to travel to five other locations labeledB 336, C 338, D 340, E 342, and F 344. For example, location C 338 maybe the prisoner's/parolee's place of work. Location B 336 may be agrocery store, and location D 340 may be a service station where theprisoner/parolee 38 may be permitted to travel to obtain gasoline andservice for his or her automobile. Locations E 342 and F 344 may bepermitted locations where the prisoner/parolee 38 may visit or stay fora period of time. Not only are the particular locations of the prisoneror parolee 38 limited to those shown in FIG. 19, but also the paths oftravel are limited to those indicated by directional arrows of FIG. 19.For example, the prisoner/parolee 38 may travel from locations A 334 toD 340 only by passing through location B 336. From location D 340, theperson may only return to location B 336 or travel to location E 342.Restricting locations and paths of travel provides a high degree ofcontrol over the activities of the prisoner/parolee 38 being tracked ormonitored.

FIG. 21 illustrates in matrix 348 the possible rights of travel depictedin the person's travel graph of FIG. 19. The rows and columns of theprisoner/parolee travel matrix 348 correspond to the locationindications in FIG. 19. A "1" indicates direct travel from the nodecorresponding to the row is permitted to the node corresponding to thecolumn. For example, bi-directional travel is permitted between node A334 and B 336 with "1" in the corresponding locations in theprisoner/parolee travel matrix 348 of FIG. 21. Similarly, travel ispermitted between locations D 340 and E 342, indicated by thecorresponding entries of "1" in the prisoner/parolee travel matrix.Prohibited direct travel between nodes A 334 and D 340 is indicated bythe presence of a "0" in the prisoner/parolee travel matrix 348. Thematrix 348 is a convenient way of storing permissible travel routes in adigital computer. Deviation from the travel routes represents violationof the permitted route of the prisoner/parolee 38 being tracked andmonitored.

FIG. 22 illustrates a travel time matrix 350 with permitted or averagetravel time recorded between the various nodes between which theprisoner/parolee 38 may travel as indicated by the prisoner/paroleetravel matrix 348 in FIG. 21. The travel time may be indicated, forexample, in minutes, and represent maximum or average travel times. Forexample, in the travel time matrix 350 of FIG. 22, the travel time fromA 334 to F 344 and from F 344 to A 334 has been indicated as 20 minutes.Multiple travel time matrices of the type illustrated in Fig. 22 may beused to indicate different travel times, such as the minimum time,average time, and the maximum time. In this way, a time limit can bespecified that will always result in an alarm being generated whenexceeded. At the same time, an "average" time matrix will permit acontinual updating of travel time on a daily basis corresponding toactual prisoner/parolee's travel times. In fact, the system is capableof "learning" expected travel times (i.e. using the learning algorithmsin FIGS. 9-12 and 15-17). Deviations from norms are noted by the controlcenter operator personnel, which may, in turn, result in increasing therate of inquiring of the parolee/prisoner 38 to determine the reason forthe deviations from expected travel times. This type of "learning"system or method permits detection of changes in individual travelhabits and may indicate unauthorized activities of the parolee orprisoner 38 being tracked. As indicated in the earlier figures, anexpert system (i.e. fuzzy logic, neural networks, reinforcementlearning, etc.) algorithm may be used to indicate abnormal travelbehavior.

FIG. 20 illustrates overlaying a ring/sector map 346 on theprisoner/parolee travel graph nodes of FIG. 19. In the ring/sector map346 of FIG. 20, the area of travel is divided into eight sectors withthree overlaying concentric ring areas. Individual nodes which theprisoner or parolee 38 may visit are then located in particular areas onthe ring/sector map. Such a mapping permits additional control of travelby restricting the sectors and/or rings in which the prisoner/parolee 38is permitted to travel. For example, using the ring/sector map of FIG.20, travel in sector S31 and S38 may be prohibited. Thus, travel fromnode A 334 to B 336 would have to be within S32 with correspondingtravel from node D 340 to B 336 avoiding sector S31. Similarly, totravel from nodes D 340 to E 342, the prisoner or parolee 38 will haveto travel through sector S28, avoiding S38.

Travel between nodes A 334 and E 342 may require passing sector S37.This restriction of travels enables identification of areas where theprisoner or parolee 38 is prohibited from visiting. For example, sectorS31 and S38 may be areas of high crime or drug/substance abuse, makingit more desirable to keep a particular prisoner or parolee 38 out ofthose areas. Also, a sector travel matrix, similar to travel matrix asshown in FIG. 21, may be used to define permitted and prohibited travelsectors for a prisoner/parolee.

FIG. 23 depicts a location dwell time matrix 352. The entries in thematrix 352 indicate the maximum time that the particular prisoner orparolee 38 may spend at each of the designated locations. Node A 334 isthe homebase of the prisoner or parolee 38, and no entry is recorded inthe corresponding location. As indicated in the figure, for example, theprisoner/parolee 38 may spend 20 minutes at node B 336, 480 minutes atnode C 338, 10 minutes at node D 340, 240 minutes at node E 342, and 300minutes at node F 344, and so forth. Multiple versions of the matrix 352may be kept in the prisoner/parolee control center 42 of FIGS. 3 and 5.Different matrices 352 may be used to indicate minimum times, averagetimes, and maximum times that the prisoner or parolee 38 is expected tostay at different locations. Deviation from these times may cause thegeneration of warning signals from control center personnel resulting ina dispatch of police or security forces or, at least, inquiry of theprisoner or parolee 38 as to why duration of the stay at a particularlocation has deviated from previously recorded maximum, minimum, oraverage values. In a manner similar to that described above for thetravel time matrix 350, the location dwell time matrix 352 may beupdated periodically to indicate average dwell times at individuallocations. In this way, the prisoner/parolee tracking and monitoringsystem may "learn" the behavior of a given prisoner or parolee 38 withrespect to the time spent at individual locations. Once again deviationsfrom expected or average times may be noted and appropriate inquiries orwarning signals generated.

FIG. 24 illustrates a ring/sector time matrix 354 used to record thetimes which the prisoner or parolee 38 is permitted to spend in each ofthe ring/sector areas 354 of FIG. 20. If the prisoner/parolee 38 is notpermitted in a given ring/sector area, the corresponding entry in thematrix 354 in FIG. 24 is zero. As indicated in matrix 354, the time T₀₀(i.e. as shown in FIG. 24, T₁₂, T₁₅, T₂₁, T₂₂, T₂₇, T₂₈, T₃₂, T₃₅, T₃₇)is entered in the corresponding column and row. As described above forthe travel time matrix 350 and location dwell time matrix 352, multipleentries of the matrix 354 may be maintained in the storage of theprisoner/parolee control center 42 to record, for example, minimum,average, and maximum ring/sector times. Exceeding specified timeintervals will result in generation of appropriate warning and/orinquiry messages depending upon the circumstances encountered. Onceagain, the average time spent in each ring/sector may be periodicallyupdated to reflect change in the travel situation for the prisoner orparolee 38 being tracked. In this way, the prisoner/parolee tracking andwarning system "learn" expected travel times in individual ring/sectorareas which then may be compared to reported times to detect changes ordeviations from expected behavior. Appropriate warning and dispatchmessages may be issued depending on the circumstances.

The above examples illustrate that the data defining a set of allowedactivities for each of the several classes of individuals 38 to bemonitored includes but is not limited to permitted travel data,permitted location data, permitted location dwell time data, andpermitted travel path data.

FIG. 25 illustrates a flow diagram 356 for the prisoner/parolee trackingand monitoring systems and methods herein disclosed. The flow diagram356 corresponds to the operation of the prisoner/parolee sensor orprocessor unit 52 of FIG. 4 as described above. As indicated in FIG. 25,the prisoner/parolee tracking and monitoring system periodically obtainsGPS coordinates at block 358 for the prisoner/parolee being tracked andmonitored. At block 360 in FIG. 25, any movements of theprisoner/parolee 38 are computed, including movements corresponding totravel times between permitted locations such as those indicated in FIG.19 and discussed above. At block 362 of FIG. 25, the location dwell timematrix 362 is updated reflecting time spent at a given location asappropriate.

Decision block 364 decides whether or not the dwell times in locationmatrix exceed specified time limits or orders. If the dwell times doexceed those limits, control is transferred to block 382 to transferalarm/notation messages and corresponding matrix information to theprisoner/parolee control center 42. If the limits are not exceeded atblock 364, then the travel time matrix is updated at block 366 and atest 368 is conducted to determine whether or not the travel timesexceed those specified in matrix 350 of FIG. 22 described above. Onceagain, if the limits are exceeded, control is transferred to block 382for transmission of alarm/notation messages and matrix data to theprisoner/parolee tracking and monitoring control center 42. If thelimits are not exceeded, the travel ring/sector time matrix 354 isupdated at block 370 and a test at decision block 372 is made todetermine if limits as specified in matrices are exceeded.

Similarly, at block 374, the prisoner/parolee travel matrix 348 isupdated; comparisons to previous matrices are made at decision block 376to determine whether or not the prisoner/parolee 38 being tracked ormonitored has traveled to an unauthorized destination as determined fromthe travel matrix. If such a determination is made, control istransferred to block 382 for transmission of appropriate alarm/notationmessages and updated matrix values to prisoner/parolee control center42.

At block 378, physical sensor analysis, using, for example,perspiration, breath, and heart rate sensors are performed as indicatedin FIG. 25 and discussed above. If the test at decision block 380indicates the physical sensor values are outside of acceptable limitsindicating an agitated state or unacceptable use of drugs or alcohol,control is again transferred to block 382 for transmission ofalarms/notations and matrix measurement parameters to control center 42.The flow diagram 356 of FIG. 25 includes the test at decision block 384to compare the time T to determine whether or not it is greater than orequal to a preset value K. If T does not exceed the specified time K,then control is passed from decision block 384 directly to delay element388 which has a specified delay T corresponding to intervals formonitoring the prisoner or parolee 38 using the herein disclosedtracking and monitoring method. When the specified delay time has beenreached, the overall process of FIG. 25 is repeated with the variousmatrices again being updated and alarm signals generated if appropriate.If T does exceed the specified time K, then all matrices and data aretransmitted to the prisoner/parolee control center 42 at block 386 forupdating prisoner/parolee records for comparison purposes and subsequentmonitoring and control operations. Control is passed from decision block384 to transmit matrix element 386 and then to delay element 388 whichhas a specified delay T corresponding to intervals for monitoring theprisoner or parolee 38 using the herein disclosed tracking andmonitoring method. When the specified delay time has been reached, theoverall process of FIG. 25 is repeated with the various matrices againbeing updated and alarm signals generated if appropriate. In thismanner, the system may monitor the movement and location of prisonersand parolees 38 at periodic intervals. Also, depending upon warningsignals that may be generated, delay time T at block 388 may beadaptively decreased to accommodate situations requiring increasedmonitor frequencies if suspicious or unexpected activity is detected.

FIGS. 26-29 show specific features and examples of implementingprisoner/parolee learning algorithms which make use of expert systemssuch as artificial intelligence, reinforcement learning, fuzzy logic,and neural networks.

FIG. 26 shows a reinforcement learning model 390 for the presentPrisoner/Parolee Behavior Tracking/Monitoring System (PBTMS) in blockdiagram form. The model 390 has a PBTMS reinforcement learning agent(RLA) of block 392. The current state (CS) of the prisoner/paroleebehavior is outputted from the RLA to block 394. The CS provides thestate/status of the prisoner/parolee behavior and also provides thestate/status of the course of action. The CS is fed into a current stateinput (CSI) indicator at block 396 which reflects the perception/view ofthe PBTMS agent to the current state (CS). The CS is also fed to ascalar reinforcement signal (SRS) at block 398 which provides the levelof changes or adjustments that need to be performed or done on the CS.The CSI and SRS are factored into the CS to provide behaviormodification (BM) for the PBTMS at block 400.

For example, by continuously monitoring a prisoner/parolee and bycontinuously updating the databases with information from theprisoner/parolee and from various sources that maintain a currentcomprehensive knowledge-based database of significant prisoner/paroleeknowledge relevant to prisoner/parolee behavior, the trainablealgorithms may be dynamically, empirically retrained by reshaping thealgorithms through trial and error based on the original and updatedinformation to optimize the accuracy of the preferred output. In suchcase, the trainable algorithm is a direct, adaptive, optimal control.The PBTMS then executes action(s) (A) to reward or punishprisoner/parolee behavior (A) at block 402 based on the BM. Theaction(s) A are fed back to the RLA at block 392, and a new currentstate (CS) of the prisoner/parolee behavior and current state of thecourse of action is outputted from the RLA at block 392. The new currentstate (CS) is also fed back to RLA to update the RLA with the newstate/status of the prisoner/parolee behavior and the new state/statusof the course of action.

FIG. 27 shows an overall PBTMS reinforcement learning software algorithm404 that is executed by the PBTMS hardware. The PBTMS reinforcementlearning algorithm starts at block 406. The PBTMS overall software agentinterfaces with the user at block 408. The PBTMS overall software agentdetermines, categorizes, and/or analyzes prisoner/parolee behavior atblock 410. The PBTMS overall software agent directs use of the PBTMSsoftware sub-agent(s) based on the determination, categorization, and/oranalysis of prisoner/parolee behavior based on prisoner/parolee historyand data at block 412.

At block 414, the PBTMS overall software agent determines and executesthe best or optimal course of actions(s) using its reinforcementlearning algorithm(s). The PBTMS overall software agent determineswhether the prisoner/parolee behavior has been satisfactorily correctedat block 416. If the prisoner/parolee behavior has not beensatisfactorily corrected, then the PBTMS overall agent learns or updatesits reinforcement learning with the current state of theprisoner/parolee behavior and the current state of the course of actionat block 418, and the overall algorithm 404 loops back to block 414 todetermine and execute the next best or optimal course of action(s). Ifthe PBTMS overall software agent has satisfactorily corrected theprisoner/parolee behavior, then the overall algorithm 404 ends at block420.

FIG. 28 shows the artificial intelligent (reinforcement learning) parolesub-agents used in the reinforcement learning algorithm. The sub-agentsare set up in a way so that low and lower level agents report to middlelevel agents and the middle level agents report to higher level agents.As shown in FIG. 28, sub-agents funnel towards an output that indicateswhether the prisoners/parolees behavior should be rewarded or punished.

One type of lowest level agent is the location type sub-agent. FIG. 28shows that various location types 424 are able to be defined. Theselocation types include but are not limited to work, school, home,stores, gas stations, parks, banks, other business and recreationallocations etc. These location types are fed into a location/dwell timehistory sub-agent 430. Also, another type of lowest level agent is thecriminal offense type sub-agent 426. FIG. 28 shows that various criminaloffense types 426 are able to be defined. These criminal offense typesinclude but are not limited to murder, robbery, theft, burglary, arson,kidnapping, rape, sex offense, drug offense, alcohol related offense,etc. These criminal offense types are fed into a criminal historysub-agent 436.

The next level of agents are lower level agents. Weights are assigned tothe various low level agents. The lower level agents consider theweights of any inputted low level agents in determining the weight to beassigned to that corresponding lower level agent.

The location/dwell time history agent 430 and the criminal offense typesagent 436 are two examples that were mentioned before that are lowerlevel agents as shown in FIG. 28. Other examples of lower level agentsare further shown in FIG. 28, and they include but are not limited tothe following: Background, Age, Sex, etc., Sub-Agent 428; Time Served AtParole Level Sub-Agent 432; Parole Level Sub-Agent 434; Criminal HistorySub-Agent 436; and Last Offense Type Sub-Agent 438.

These lower level agents are fed into a middle level agent that is anIndividual Based Parole Sub-Agent 440. Weights are assigned to thevarious lower level agents. The individual based parole sub-agent 440considers the weights of the various lower level agents in determiningthe weight to be assigned to the individual prisoner/parolee behavior.Another middle level agent is the aggregate based parole sub-agent 442.The aggregate based parole sub-agent 442 is statistically compiled dataand algorithm(s) as to the degree of match with the prisoners/paroleeswho re-committed a crime. The aggregate based parole sub-agent 442considers the statistically compiled data and algorithm(s) and assigns aweight based on the degree of match between an aggregate ofprisoners/parolees who recommitted the same or similar crime indetermining the weight to be assigned to the aggregate prisoner/paroleebehavior.

The outputs from the individual based parole sub-agent 440 and theoutputs from the aggregate based parole sub-agent 442 are fed into ahigher level agent 448, which is the final combined parole agent 448.The outputs from the individual based parole sub-agent 440 and theaggregate based parole sub-agent 442 are weighted, and these weightedoutputs are considered by the final combined parole agent 448 indetermining whether the parole level for that prisoner/parolee should goup a level, remain at the same level, or go down a level as shown inFIG. 28. In FIG. 28, a "+1" represents the moving of the parole levelfor the prisoner/parolee up a level, a "0" represents the maintaining ofthe parole level for the prisoner/parolee at the same level, and a "-1"represents the moving of the parole level for the prisoner/parolee downa level. FIG. 28 also shows a general time-line indicating that theparole sub-agents move from past data to present data to predicted data.

FIG. 29 shows an aggregate probability identifier chart 452. The chart452 is derived from a compilation of statistics from an aggregate numberof prisoners/parolees 38. The probability graph of FIG. 29 chartsdifferent location types 424 against different crime types 426. Thelocation types 424, as shown by examples in FIG. 29, include but are notlimited to banks, bathrooms, casinos, fast food restaurants, bars, ruralareas, gas stations, parking lots, residential areas, streets andsidewalks, parks, convenient stores, and schools. The crime types, asshown by example in FIG. 29, include but are not limited to fraud, autotheft, child molestation, murder, shoplifting, vandalism, robbery,arson, prostitution, kidnaping, rape, drugs, and driving under theinfluence. For each crime type, a probability is assigned to aparticular location type 424 wherein the probability represents thechance that the particular crime type 426 will be committed at thatparticular location type 424.

The following are location type 424 examples for crime types 426. InFIG. 29, the first crime type 426 shown is fraud. For fraud, the highprobability areas that a fraud crime may be committed is at banks orbank locations, in which a 100% probability is assigned to thatlocation. In other words, fraud related crimes have a high probabilityor possibility of occurring at banks, and if a prisoner/parolee 38 isspending an unusual or abnormal amount of time at a bank, then thereinforcement learning algorithms of the prisoner/parolee tracking andmonitoring system make note of or take appropriate action in response tothe prisoner/parolee behavior. The middle probability areas that a fraudcrime may be committed at some locations are as follows: at bathrooms(50%); at casinos (60%); at fast food restaurants (50%); at bars (50%);at residential areas (50%); at streets and sidewalks (50%). These middleprobability areas are locations at which if a prisoner/parolee 38 isspending an unusual or abnormal amount of time, then the reinforcementlearning algorithms of the prisoner/parolee tracking and monitoringsystem may or may not make note of the behavior or take appropriateaction and may or may not determine or conclude whether theprisoner/parolee is committing that type of crime or violation. The lowprobability areas that a fraud crime may be committed at some locationsare as follows: rural areas (3%); gas stations (5%); parking lots (10%);parks (1%); convenient stores (1%); school (1%). The low probabilityareas are locations at which if a prisoner/parolee 38 is spending anunusual or abnormal amount of time, then reinforcement learningalgorithms of the prisoner or parolee tracking/monitoring system willnot make note of the behavior or take appropriate action and will notdetermine or conclude whether the prisoner/parolee 38 is committing thattype of crime or violation.

Furthermore, the second crime type 426 shown is automobile theft. Forautomobile theft, the high probability areas that an auto theft crimemay be committed are at some of the following locations: at parking lots(100%); at residential areas (80%); at streets and sidewalk areas(100%); at parks (90%); and convenient stores (70%). In other words, atthese locations, auto theft crimes have a high probability orpossibility of occurring, and if a prisoner/parolee 38 is spending anunusual abnormal amount of time at these locations, then thereinforcement learning algorithms of the prisoner or paroleetracking/monitoring system makes note of or takes appropriate action inresponse to the prisoner/parolee behavior. The middle probability areasthat an automobile theft crime may be committed at some locations are asfollows: at gas stations (40%) and at schools (60%). The middleprobability areas are locations at which if a prisoner/parolee 38 isspending an unusual or abnormal amount of time, then the reinforcementlearning algorithms of the prisoner or parolee tracking/monitoringsystem may or may not make note of the behavior or take appropriateaction and may or may not determine or conclude whether theprisoner/parolee 38 is committing that type of crime or violation. Thelow probability areas that an automobile theft crime may be committed atsome locations are as follows: at banks (0%); at bathrooms (0%); atcasinos (5%); at fast food restaurants (0%); at bars (5%); and at ruralareas (10%). The low probability areas are locations at which if aprisoner/parolee 38 is spending an unusual or abnormal amount of time,then the reinforcement learning algorithms of the prisoner or paroleetracking/monitoring system will not make note of the behavior or takeappropriate action and will not determine or conclude whether theprisoner/parolee 38 is committing that type of crime or violation.

Additionally, the third crime type 426 shown is child molestation. Forchild molestation, the high probability areas that a child molestationcrime may be committed are at some of the following locations: atparking lots (100%); at residential areas (100%); at streets andsidewalks (100%); at parks (100%); and at school (100%). In other words,at these locations, child molestation crimes have a high probability ofoccurring, and if a prisoner/parolee 38 is spending an unusual orabnormal amount of time at these locations, then the reinforcementlearning algorithms of the prisoner or parolee tracking/monitoringsystem makes note of or takes appropriate action in response to theprisoner/parolee behavior. The middle probability areas that a childmolestation crime may be committed at some locations are as follows: atbathrooms (50%); at casinos (50%); at fast food restaurants (50%); andat rural areas (50%). The middle probability areas are locations atwhich if a prisoner/parolee 38 is spending an unusual or abnormal amountof time, then the reinforcement learning algorithms of the prisoner orparolee tracking/monitoring system may or may not make note of thebehavior or take appropriate action and may or may not determine orconclude whether the prisoner/parolee 38 is committing that type ofcrime or violation. The low probability areas that a child molestationcrime may be committed at some locations are as follows: at banks (0%);at bars (10%); at gas stations (30%); and at convenient stores (30%).The low probability areas are locations at which if a prisoner/paroleeis spending an unusual or abnormal of time, then the reinforcementlearning algorithms of the prisoner or parolee tracking/monitoringsystem will not make note of the behavior or take appropriate action andwill not determine or conclude whether the prisoner/parolee 38 iscommitting that type of crime or violation.

The other crime types 426 are assigned probabilities to each locationtype 424 in the similar way and manner that the fraud, auto theft, andchild molestation crimes have been assigned probabilities. Thereinforcement learning algorithms of the prisoner/parolee tracking ormonitoring system uses these probabilities to predict futurecrimes/violations of various prisoners/parolees 38. The algorithms usethese probabilities to make the determination of whether or not a crimeor violation is being committed. The probabilities may be based oninformation from the individual prisoner/parolee information from anaggregate number of prisoners/parolees 38. The probabilities and weightsare adjusted by the reinforcement learning algorithms based on analysis,conclusions, and results. The weights and probabilities may be adjustedaccording to the individual prisoner/parolee behavior, the aggregatepattern and behaviors of prisoners/parolee 38, the specific environmentand demographics (i.e. certain crimes may occur more frequently atspecific locations than other locations), and the aggregate or overallenvironment and demographics.

Scope of Disclosure

The preferred embodiments of the inventions are described in the figuresand detailed description. Unless specifically noted, it is applicants'intention that the words and phrases in the specification and claims begiven the ordinary and accustomed meaning to those of ordinary skill inthe applicable art(s). If applicant intend any other meaning, they willspecifically state they are applying a special meaning to a word orphrase.

Likewise, applicants' use of the word "function" in the DetailedDescription is not intended to indicate that they seek to invoke thespecial provisions of 35 U.S.C. Section 112, paragraph 6 to define theirinvention. To the contrary, if applicants wish to invoke the provisionof 35 U.S.C. Section 112, paragraph 6, to define their invention, theywill specifically set forth in the claims the phrases "means for" or"step for" and a function, without also reciting in that phrase anystructure, material or act in support of the function. Moreover, even ifapplicant invokes the provisions of 35 U.S.C. Section 112, paragraph 6,to define their invention, it is applicant's intention that theirinventions not be limited to the specific structure, material, or actsthat are described in the preferred embodiments. Rather, if applicantsclaim their invention by specifically invoking the provisions of 35U.S.C. Section 112, paragraph 6, it is nonetheless their intention tocover and include any and all structures, materials or acts that performthe claimed function, along with any and all known or later developedequivalent structures, materials, or acts for performing the claimedfunction.

For example, there are disclosed several algorithms for tracking,monitoring, and/or learning prisoner behavior. In its preferred form,applicant divides these algorithms into several steps. However, withappropriate structuring and/or programming well known to those ofordinary skill in the art, the inventions can be implemented using fewersteps. Thus, it is not applicants' intention to limit their invention toany particular form of algorithm.

Additionally, there are disclosed several algorithms for tracking,monitoring, and/or learning prisoner behavior that use expert systems(i.e. including but not limited to reinforcement learning, artificialintelligence, fuzzy logic, neural networks, etc.). In its preferredform, applicant divides these algorithms into several operations.However, with appropriate structuring and/or programming well known tothose of ordinary skill in the art, the inventions can be implementedusing fewer operations. Thus, it is not applicants' intention to limittheir invention to any particular form of algorithm. Also, theinventions described herein are not to be limited to specific expertsystems disclosed in the preferred embodiments, but rather, are intendedto be used with any and all such systems.

Similarly, the inventions described herein are not to be limited tospecific communications hardware, devices, or methods (i.e. whichinclude but are not limited to radio, two-way radio, cellular,telephone, modem, satellite communications, etc.) disclosed in thepreferred embodiments, but rather, are intended to be used with any allsuch applicable communications hardware, devices, or methods forcommunicating between the prisoner/parolee, a location of theprisoner/parolee, and/or the control center of the present invention.

As a further example, the present inventions make use of GPS satellitelocation technology for deriving prisoner location and motion trajectoryparameters for use in the prisoner tracking and warning systems andmethods herein disclosed. The inventions described herein are not to belimited to specific GPS devices disclosed in the preferred embodiments,but rather, are intended to be used with any and all such applicablesatellite or ground based location devices, systems and methods, as longas such devices, systems and methods generate input signals that can beanalyzed by a computer to detect and accurately quantify prisoner orparolee location and motion parameters.

Likewise, for example, the present inventions generate prisoner orparolee monitoring or surveillance information, including analysis ofbody functions such as heart rate or perspiration chemical compositionfor analysis. The inventions described herein are not to be limited tospecific monitoring or sensing devices disclosed in the preferredembodiments, but rather, are intended to be used with any and allapplicable monitoring or sensing devices, as long as the device cangenerate an input signal that can be analyzed by a computer to detectdangerous or unusual situations. Accordingly, the words "monitor" and"sensor" as used in this specification should be interpreted broadly andgenerically.

Further, there are disclosed several computers, controllers, andcomputer hardware, that perform various control operations. The specificform of computer or computer hardware is not important to the invention.In its preferred form, applicant divides the computing and analysisoperations into several cooperating computers or microprocessors.However, with appropriate structuring and/or programming the inventionscan be implemented using fewer, high powered or specialized computers,processors, or other computer hardware. Thus, it is not applicants'intention to limit their invention to any particular form computer.

Further example exist throughout the disclosure, and it is notapplicants' intention to exclude from the scope of their invention theuse of structures, materials, or acts that are not expressly identifiedin the specification, but nonetheless are capable of performing aclaimed function.

The inventions set forth above are subject to many modifications andchanges without departing from the spirit, teachings, or essentialcharacteristics thereof. The embodiments explained above should beconsidered in all respects as being representative rather than beingrestrictive of the scope of the invention as defined in the appendedclaims.

We claim:
 1. A method of monitoring and learning a subject's behaviorcomprising the acts of:a) creating and storing in a memory of amonitoring station computer a first file including reference behaviordata defining several classes of individuals to be monitored, includingat least one class to which the subject belongs; b) creating and storingin the monitoring station computer memory a second file includingbehavior data defining the subject to be monitored; c) defining andprogramming the monitoring station computer with data defining a set ofallowed activities for each of the several classes of individuals to bemonitored; d) defining and programming the monitoring station computerwith data defining a set of allowed activities that are specific for thesubject to be monitored, wherein the allowed activities includepredefined routes and times of travel in a location remote from themonitoring station computer; e) attaching a remote monitoringtransmitter and receiver to the subject, wherein the receiver cooperateswith a satellite global positioning system to determine the subject'scurrent location as the subject moves about in the area located remotefrom the monitoring station computer; f) periodically transmitting datadefining the subject's location at a specific time from the remotemonitoring transmitter and receiver to the monitoring station computer;g) computer analyzing the data transmitted from the remote monitoringtransmitter by comparing the data responsive to the subject's currentlocation and time with the set of allowed activities that are specificfor the subject and determining if there are any variations from theallowed activities; h) generating a first alarm signal responsive to anydetermined variation from the allowed activities; i) further analyzingany such first alarm signal defining the determined variation from theallowed activities using an expert system programmed to recognize acontinuum of degrees of alarms based on a comparison of the determinedvariation, the behavior data defining the subject to be monitored, andthe reference behavior data defining the class of individuals to whichthe subject belongs; and j) generating from the expert system a secondalarm signal defining a specific recommended course of action that isappropriate for the determined variation, the subject's specificbehavior data, and the data defining the class of individuals to whichthe subject belongs.
 2. The method of claim 1 further comprising theacts of:a) when any alarm has occurred more frequently analyzing thedata defining the subject's current location and time, the set ofallowed activities that are specific for the subject and the secondalarm signal defining the recommended course of action; b) determiningwhether the second alarm condition has changed by becoming more or lesscritical; and c) if necessary, modifying the second alarm condition toreflect any determined change.
 3. The method of claim 2 furthercomprising updating the data defining the allowed activities that arespecific to the subject to include data defining the behavior of themonitored subject that caused the issuance of the alarm.
 4. The methodof claim 2 further comprising, when the second alarm has been modifiedby becoming less critical, modifying the data defining the allowedbehavior of the monitored subject.
 5. The method of claim 1 wherein thereference behavior data defining several classes of individuals to bemonitored comprises:criminal behavior data relating to the severalclasses of individuals.
 6. The method of claim 1 wherein the referencebehavior data defining several classes of individuals to be monitoredcomprises:criminal history and criminal record data from the severalclasses of individuals.
 7. The method of claim 1 wherein the referencebehavior data defining several classes of individuals to be monitoredcomprises:parole level information relating to the several classes ofindividuals.
 8. The method of claim 1 wherein the reference behaviordata defining several classes of individuals to be monitoredcomprises:data relating to a number of different types of crimes fromthe several classes of individuals.
 9. The method of claim 1 wherein thereference behavior data defining several classes of individuals to bemonitored comprises:data relating to a defined deviated behaviorstandard derived from the several classes of individuals.
 10. The methodof claim 1 wherein the reference behavior data defining several classesof individuals to be monitored comprises:crime probability data thatcompares various crime types with various location types wherein a crimeprobability for each of the various crime types is determined andassigned for each of the various location types.
 11. The method of claim1 wherein the behavior data defining the subject to be monitoredcomprises:criminal behavior data relating to the subject.
 12. The methodof claim 1 wherein the behavior data defining the subject to bemonitored comprises:criminal history and criminal record data relatingto the subject.
 13. The method of claim 1 wherein the behavior datadefining the subject to be monitored comprises:parole level informationrelating to the subject.
 14. The method of claim 1 wherein the behaviordata defining the subject to be monitored comprisesdata relating totypes of crimes committed by the subject.
 15. The method of claim 1wherein the data defining a set of allowed activities for each of theseveral classes of individuals to be monitored comprises:permittedtravel data for each of the several classes of individuals to bemonitored.
 16. The method of claim 15 wherein the data defining a set ofallowed activities for each of the several classes of individuals to bemonitored comprises:permitted location data for each of the severalclasses of individuals to be monitored.
 17. The method of claim 15wherein the data defining a set of allowed activities for each of theseveral classes of individuals to be monitored comprises:permittedlocation dwell time data for each of the several classes of individualsto be monitored.
 18. The method of claim 15 wherein the data defining aset of allowed activities for each of the several classes of individualsto be monitored comprises:permitted travel path data for each of theseveral classes of individuals to be monitored.
 19. The method of claim1 wherein the remote monitoring transmitter and receiver includes anaudible alarm, and further comprising transmitting a signal from themonitoring station computer to the monitoring transmitter and receiverattached to the subject, which signal activates the audible alarm toindicate to the subject that an alarm condition has been triggered. 20.The method of claim 1 further comprising operating an expert system toanalyze the first and second alarm conditions, and the data thatgenerated the alarm conditions, and modifying the first and second datafiles to reflect learned activities that either should or should notgenerate an alarm.
 21. The method of claim 20 wherein the act ofoperating the expert system comprises:a) using a criminology model towhich the determined variation from the allowed activities is compared;b) determining whether the variation from the allowed activitiesconstitutes a crime based on the comparison; c) recording the variationfrom the allowed activities into the second file of the monitoringstation computer memory; and d) recommending any necessary appropriateaction responsive to the determination of the variation from the allowedactivities.
 22. The method of claim 1 further comprising operating anexpert system to learn behavior that unnecessarily generated an alarm,and modifying the behavior data defining the subject to be monitored.23. The method of claim 1 further comprising operating an expert systemto learn behavior that unnecessarily generated an alarm, and modifyingthe data defining the set of allowed activities that are specific forthe subject to be monitored so that the alarm is not generated in thefuture.
 24. The method of claim 1 further comprising continuing toperiodically monitor the subject's behavior, and after a predefinedperiod without an alarm being generated, modifying the data defining theset of allowed activities that are specific for the subject to bemonitored to provide for an increased area and longer allowed time oftravel.
 25. The method of claim 24 further comprising communicating theincrease area and longer time of travel to the remote monitoringtransmitter and receiver attached to the subject.
 26. The method ofclaim 1 further comprising continuing to periodically monitor thesubject's behavior, and after an alarm is generated, modifying the datadefining the set of allowed activities that are specific for the subjectto be monitored to provide for a decreased area and shorter time oftravel.
 27. The method of claim 26 further comprising communicating thedecreased area and shorter allowed time of travel to the remotemonitoring transmitter and receiver attached to the subject.
 28. Themethod of claim 1 further comprising:a) using the remote monitoringtransmitter and receiver attached to the subject to monitor a physicalattribute of the subject; b) transmitting defining the monitoredphysical attribute of the subject from the remote monitoring transmitterand receiver to the monitoring station computer; c) and wherein computeranalyzing the data includes comparing the data defining the subject'smonitored location, time and physical attributes with the set of allowedactivities that are specific for the subject and determining if thereare any variations from the allowed activities.
 29. The method of claim28 wherein the act of using the remote monitoring transmitter andreceiver to monitor the physical attribute of the subject includes theact of:using the remote monitoring transmitter and receiver to monitorspeech of the subject.
 30. The method of claim 28 wherein the act ofusing the remote monitoring transmitter and receiver to monitor thephysical attribute includes the act of:using the remote monitoringtransmitter and receiver to monitor alcoholic levels of the subject. 31.The method of claim 28 wherein the act of using the remote monitoringtransmitter and receiver to monitor the physical attribute includes theact of:using the remote monitoring transmitter and receiver to monitorheart rate of the subject.
 32. The method of claim 28 wherein the act ofusing the remote monitoring transmitter and receiver to monitor thephysical attribute includes the act of: using the remote monitoringtransmitter and receiver to monitor breath rate of the subject.
 33. Themethod of claim 28 wherein the act of using the remote monitoringtransmitter and receiver to monitor the physical attribute includes theact of:using the remote monitoring transmitter and receiver, to monitorperspiration of the subject.